Atmospheric Pollution Research最新文献

This study aimed to determine the metals in ambient PM_2.5 in the expanding industrial metropolitan area of Rayong Province for health risk assessment and source apportionment from May 2022 to April 2023, covering wet and dry seasons. The mean annual PM_2.5 concentration was 15.2 ± 12.0 μg m⁻³, whereas that of wet and dry seasons were 8.4 ± 5.4 μg m⁻³ and 21.8 ± 12.9 μg m⁻³, respectively. The annual PM_2.5 level exceeded the limit set by the World Health Organization (WHO) (5 μg m⁻³) and the standard of Thailand (15 μg m⁻³). A substantial decrease in the Cd, Pb, Zn, Cu, Fe, Mn, and K concentrations was observed during the wet season compared with that of the dry season. The levels of annual Cr in PM_2.5 were 40 times higher than the WHO limit. Cd, Pb, and Zn are tracers of anthropogenic activities. Using the enrichment factor (EF) and I_geo, the contamination of As, Cd, Pb, and Zn suggested that the initial Eastern Economic Corridor (EEC) in Rayong Province was highly polluted. The results of the non-carcinogenic risk indicated that human health was notably affected by toxic metals in PM_2.5, and the Cr-related carcinogenic risk in PM_2.5 exposure suggested a safe or reasonable risk level (10⁻⁶ to 10⁻⁴). Exposure to toxic metals in PM_2.5 increases the risk of developing cancer in adults, potentially owing to the accumulation of these metals within the tissues in the body. Positive matrix factorisation (PMF) suggested that the source apportionment of PM_2.5-bound heavy metals was motor vehicles (34.7%), industrial activities (26.3%), biomass burning (22.7%), and road dust (18.5%).

Despite continued actions to abate harmful air pollutant emissions, air pollution is still a worldwide concern, yet apportioning individual shares of responsibility for pollution is challenging. Here, we present a spatial approach combined with microscopy, elemental composition, and Pb isotopes to trace particulate matter (PM) emissions related to a steel manufacturing plant in Middletown, Ohio. Evergreen leaves were collected in nine sites situated 18 and 32 km upwind and 0–35 km downwind from the steel plant. The relative abundance and size range of spherical Fe-rich particles, as indicators of the steel factory's emissions, were quantified using SEM/EDS. Elemental compositions and Pb isotopes were used for PM source apportionment. The SEM/EDS quantification method was effective for steel particles, while it was less suitable for quantifying fly ash abundances owing to its limitations in detecting ultrafine PM, where fly ash particles are prevalent. Pb isotopes indicated that the average leaf-level PM mass originating from glacial till, steel plant, gasoline, and fly ash, were 44 ± 23, 34 ± 30, 33 ± 17, and 18 ± 11 mg m⁻², respectively, highlighting the steel plant and gasoline as the primary anthropogenic PM sources. Strong correlations between steel spherule mass estimated by MixSIAR and its relative proportion quantified through microscopic investigations (r = 0.94) and pollution load index (r = 0.89) provide support for source apportionment using isotopic methods. The steel spherules quantity decreased exponentially with distance with the steel plant's effective PM footprint extending approximately 32 and 40 km upwind and downwind, respectively, emphasizing its ongoing environmental impact despite pollution control measures.

In the urban area of Hachinohe, Japan, PM_2.5 sampling was carried out from May 2015 to February 2021. The average concentration of PM_2.5 during the entire sampling period was approximately 11.7 μg m⁻³, with 4.4 μg m⁻³ for water soluble ions, 3.3 μg m⁻³ for carbonaceous species, 0.5 μg m⁻³ for trace metals, and 3.5 μg m⁻³ for other species. Based on this comprehensive component information, eight sources were quantitatively explored, among which ship emissions (29%), traffic emissions (19%), and secondary organic aerosols (15%) had relatively high contributions for PM_2.5 concentration level. The health risk assessment indicated that the children in Hachinohe City faced serious non-carcinogenic and carcinogenic risks, with corresponding values of 8.0 for HI and 1.2 × 10⁻⁴ for CR. The pollutants from ship emissions, secondary nitrates plus coal combustion, and industrial emissions should be of concern. High risk metals included Pb, As, Sb, V, and Cr(VI). Specifically, ship emissions exhibited the highest concentration (5.5 μg m⁻³) and health risks (HI = 2.2 and CR = 3.0 × 10⁻⁵) in summer; priority should be given to controlling pollution in the Port of Hachinohe. The other two sources had the highest concentration in winter (2.0 and 0.5 μg m⁻³) and were mainly influenced by the polluted air masses from Akita Prefecture, with HI values of 2.4 and 2.5 and CR values of 4.9 × 10⁻⁵ and 3.2 × 10⁻⁵, respectively. Overall, our study comprehensively revealed the characteristics of PM_2.5 in Hachinohe City and conducted an in-depth investigated into its causes of pollution. This information could serve as a scientific basis for developing specific strategies to improve air quality.

Deposition of ambient black carbon (BC) aerosols over snow-covered areas reduces surface albedo and accelerates snowmelt. Based on in-situ atmospheric BC data and the WRF-Chem model, we estimated the dry and wet deposition of BC over the Yala glacier of the central Himalayan region in Nepal during 2016–2018. The maximum and minimum BC dry deposition was reported in pre- and post-monsoon respectively. Approximately 50% of annual dry deposition occurred in the pre-monsoon season (March to May) and 27% of the annual dry deposition occurred in April. The total dry BC deposition rate was estimated as ∼4.6 μg m⁻² day⁻¹ providing a total deposition of 531 μg m⁻² during the pre-monsoon season. The contribution of biomass burning and fossil fuel sources to BC deposition on an annual basis was 28% and 72% respectively. The annual accumulated wet deposition of BC was 196 times higher than the annual dry deposition. The ten months of observed dry deposition of BC (October 1, 2016 to August 31, 2017 – except December 2016) was ∼39% lower than that of WRF-Chem's estimated annual dry deposition from September 1, 2016 to August 31, 2017 partially due to model bias. The deposited content of BC over the snow surface has an important role in albedo reduction, therefore snow samples were collected from the surface of the Yala Glacier and the surrounding region in April 2016, 2017, and 2018. Samples were analyzed for BC mass concentration through the thermal optical analysis and single particle soot photometer method. The BC calculated via the thermal optical method was in the range of 352–854 ng g⁻¹, higher than the BC calculated through the particle soot photometer method and estimated BC in 2 cm surface snow (imperial equation). The maximum surface snow albedo reduction due to BC was 8.8%, estimated by a widely used snow radiative transfer model and a linear regression equation.

Deteriorating visual range (VR) can cause challenges for the transportation sector, resulting in economic and life losses. Air pollutants, smoke, fog, and many meteorological parameters such as air temperature (T), relative humidity (RH), wind speed (WS), and wind direction (WD) can contribute to light extinction and degrade VR. Advancements in geospatial technologies have triggered artificial intelligence to analyze and model the relationships among environmental and climatological parameters. This paper aims to assess the potential of supervised machine learning models for the parameterization of VR over Pakistan's diverse topography by utilizing meteorological parameters and some pollutants. The daily data from 2005 to 2020 of VR, T, RH, WS, WD, Aerosol Optical Depth (AOD), Nitrogen dioxide (NOx), Sulfate, Sulfur dioxide (SOx), and Dust were acquired. Ten machine learning models, including Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Networks (ANN), Support Vector Machine (SVM), Decision Trees (DT), Gradient Boosting Machine (GBM), Causal, Unbiased, Binned, and Intermittent, Search, and Tree (CUBIST), Multi-Layer Perceptron (MLP), Multivariate Adaptive Regression Splines (MARS), and K-Nearest Neighbor (KNN) were gauged for VR estimation. We also coupled the Bagged Extreme Gradient Boosting (BG-XG) model by combining XGB and bagging technique. BG-XG performed better than the rest of the models, with coefficients of determination of 0.90 for the training and 0.70 to 0.90 for the validation set. Degradation in the VR was highly dependent on the changes in RH followed by SOx and dust associated with anthropogenic emissions. RH, SO₄, and SO₂ emerged as the most important parameters for the VR decline. Proposed model parameters can be helpful in accurate VR projections and improving severe weather alerts, including analyzing and managing air pollution. This work will also be helpful to improve aviation and transportation safety for pilots, drivers, and automated vehicles to minimize low-visibility accidents.

Intra-seasonal oscillations (ISO) are well known to modulate the weather phenomena which in turn are known to influence the atmospheric aerosol loading. This study investigates how aerosol loading is modulated in ISO spatio-temporal scales over the Indian region using long-term satellite aerosol optical depth data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, onboard Terra Satellite. It is shown that Madden-Julian Oscillation (MJO) and Equatorial Rossby waves (ER) have the highest effect (15–20% of the mean) followed by Mixed-Rossby-gravity and Tropical depressions (MT), and Kelvin wave (KE) (5–15%). Further, a dipolar pattern in aerosol loading was observed, with poles over the Arabian Sea and the Bay of Bengal. These variabilities were found to be mainly driven by anomalous winds associated with the ISOs. Similar to aerosol, dipolar signatures in the atmospheric aerosol radiative forcing (ARF) were also observed with clearer patterns. However, the forcing poles are not centered exactly where aerosol poles were observed, indicating the effect of differential properties of aerosols on the aerosol radiative forcing. Quantitatively, at the surface level, modulation in ARF is up to 3 Wm^-2 (15%) for MJO and ER, and up to 2 Wm^-2 (5%) for KE and MT; in the atmosphere and at the top of the atmosphere, modulation is up to 2 Wm^-2 (15%) for MJO and ER, and up to 1 Wm^-2 (5%) for KE and MT.

Acid rain, characterized by pH values lower than 5.6, is a critical natural disturbance of ecosystems, which threatens the sustainability of ecosystems, agriculture, and human society worldwide. However, accurately quantifying the driving factors of acid rain remains challenging due to a changing environment of significant spatial heterogeneity. Here, we established an explainable machine-learning framework (MLF) using 19 meteorological, air pollutant, and land surface variables as model input to construct the pH values of acid rain across the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) during 2006–2021. The MLF includes Extreme Gradient Boosting (XGBoost) for acid deposition prediction and a SHapley Additive exPlanations method (SHAP) for interpreting factor importance. The results indicated that the observed increases in pH values of acid rain are predominantly controlled by the significant decreases in maximum daily sulfur dioxide (SO₂) concentration of air across GBA, with its relative contribution ranging from 16.2% to 31.9% for each city. Changes in the urbanization rate and the proximity to the coast also play significant roles in predicting the pH values of acid rain. Meteorological variables typically have minimal impact on acid rain predictions, with their contribution generally being less than 5%, indicating the complex physical process of acid rain generation. This study enhanced our comprehension of the spatial variability of acid rain drivers across a highly developed region, providing valuable insights and case studies for regions worldwide that frequently experience acid rain.

Nitrogen deposition affects the emission of biogenic volatile organic compounds (BVOCs) and thus their formation of ozone (O₃) and secondary organic aerosols (SOA). The present study employed four nitrogen concentrations (6, 10, 15, and 30 kg ha⁻¹ yr⁻¹) and two nitrogen application methods (foliar surface and root application) to investigate the short-term effects of nitrogen deposition on BVOC emissions in seedlings of Ficus virens, Ficus concinna and Ficus elastica through controlled indoor pot experiments. The results demonstrated a positive correlation between the nitrogen concentration and the emission rate of BVOCs, with leaf nitrogen application exhibiting a significantly greater impact than root nitrogen application. The net photosynthetic rate and stomatal conductance emerged as pivotal factors influencing the emission of BVOCs. The maximum incremental reactivity (MIR) and fractional aerosol coefficient (FAC) methods were employed to assess the contribution of BVOCs to O₃ formation and SOA production. Our findings indicate that isoprene emitted by seedlings from the three plant species emerged as the predominant driver for O₃ formation, while monoterpenes and sesquiterpenes played a pivotal role in SOA production.

Ongoing respiratory epidemics are raising health concerns in public transportation environments, especially in densely populated train compartments. While air circulation systems inside these compartments play a crucial role in controlling the risk of droplets carrying pathogens, the mechanisms and strategies have rarely been investigated. This study employs computational fluid dynamics (CFD) to investigate the impacts on droplet dispersion and exposure risk within passenger compartments under two prevalent air circulation modes: centralized return-centralized exhaust (CR-CE) and distributed return-centralized exhaust (DR-CE), as well as a newly proposed mode, distributed return-distributed exhaust (DR-DE). Additionally, other key influential factors, including the release source location and the respiratory jet speed, are also considered. The results indicate that the CR-CE mode exacerbates the longitudinal airflow in the passenger compartment, thereby increasing the distance of droplet transmission. In contrast, the DR-CE mode moderately restricts the range of droplet spread to a certain extent. When the release source is in the middle of the compartment, the average distance of droplet transmission can be reduced by about 30%. The proposed DR-DE mode further confines the behavior of droplet dispersion, significantly lowering the overall exposure risk for passengers. Furthermore, the results show that sneezing, compared to speaking, results in a decrease in the exposure risk peak among passengers, from approximately 95% to around 70%. The passengers in the four rows directly in front of the release source face a relatively high potential risk. These findings provide valuable insights for improving air quality and passenger safety in public transportation vehicles.

Landfill emissions, particularly methane leaks detected by satellite, are drawing great attention in the last years as a mean for evaluate their contribution to the global warming effect. In contrast, models like IPCC often overestimate landfill methane emissions, prompting verification and mitigation system evaluation. Methane's higher warming capacity than carbon dioxide underscores the importance of promoting its oxidation as it traverses landfill layers. This oxidation raises the CH₄–¹³C/¹²C ratio via bacterial biooxidation. This study quantified this fractionation using soil and surface gas samples from Spanish landfills and their degassing systems. Sampling relied on walkover surveys collecting samples in Tedlar bags, to analyze isotopic signals in the laboratory using WS-CRDS. Fractionation factors (α) ranged from 1.020 to 1.030, while the oxidized fraction (f_ox) spanned from no oxidation to 55% (δ¹³C of −45.83‰). Each ratio correlates with emission types like fugitive and dispersed emissions on plateaus, berms, slopes, sealing cracks, or pits. Understanding the methane oxidized fraction in each landfill is relevant for greenhouse gas emission model integration.